Development and application of a photogrammetry based statistical shape analysis technique for condition monitoring of rotating structures

Abstract

Large rotating structures such as wind turbine blades require specialized measurement techniques for the purpose of online condition monitoring and assessment. Contact transducers such as accelerometers and strain gauges are traditionally used to capture vibrational data that can be analysed to understand the dynamics of a system. These are however intrusive in the sense that they must be physically attached to the structure under investigation. In addition, they are point-wise in nature, implying that measurements are only captured for those specific locations where the transducer is attached. They may also alter the local structural properties at the point of attachment, including additional mass loading effects of the sensor on light structures. Optical techniques such as photogrammetry and laser vibrometry are promising alternatives that have been receiving much attention. 3D Point Tracking (3DPT) and Digital Image Correlation (DIC) constitute photogrammetric-based optical measurement techniques that have proven to be efficient for the vibration analysis of rotating machinery. In addition to complex image processing software and tracking algorithms, these two approaches typically require surface preparation in the form of markers and speckle patterns. The surface preparation typically requires a system shutdown which can be complicated and costly. Applied surface treatments also do not last throughout the lifespan of the structure and often must be reapplied. In order to track specific pixels for 3DPT and DIC, the lighting on the surface of the structure needs to be closely monitored since the tracking is based on pixel gray scale values. These requirements limit the applicability of photogrammetry as a condition monitoring tool, especially when it comes to field or outdoor full-scale testing. Photogrammetric shape-based analysis is an alternative approach that does not require prior surface preparation. By focusing on the boundary outline of a structure, the technique is a suitable candidate for outdoor investigations where consistent uniform lighting on an entire structural surface may be impossible. It can also be applied to large structures with significant levels of rigid body motions. To date, this approach has not yet been employed for dynamic analysis of machines. The concept of shape analysis is typically applied for object recognition or shape matching in applications such as Content Based Image Retrieval (CBIR). Thus a single image is captured and then analysed to be matched to another image stored in a database, for instance. This research focuses on the development and application of a shape based photogrammetric technique that can be used to capture dynamics of rotating structures without the requirement for surface preparation. The goal of the study is to develop an approach that can be used to distinguish faults in the system and classify machine behaviour for condition monitoring purposes. In this type of application, sequences of images of a structure in operation are captured, and boundary contours of an object in the images extracted. Through defining parameters that characterise contours extracted from each of these images, and then monitoring the variation of these parameters in time, the idea of shape analysis can be adopted for condition monitoring of machines as an optical non-contact measurement technique. Shape Principal Component Descriptors (SPCDs) determined by performing Shape Principal Component Analysis (SPCA) of Fourier descriptors calculated from shape signatures of the extracted contours are the parameters investigated in this study. Condition monitoring strategies for rotating structures are discussed in a literature review that highlights the importance of structural health maintenance and the shortcomings and limitations of conventional measurement techniques. Advanced optical measurement techniques that include photogrammetry and laser vibrometry are discussed to describe and illustrate the evolution of recent noncontact technologies as viable tools for condition monitoring purposes. Typical applications of photogrammetric techniques such as surface strain measurement are highlighted. Instances that demonstrate the successful use of optical approaches to capture dynamics of rotating structures are discussed. This lays out a foundation onto which the necessity of advancing optical based strategies into more suited techniques for industrial application purposes is built. The concept of shape analysis is introduced and its SPCDs investigated for a 2D shape that has in-plane form variations associated with it. On application to a physical rotor system, it is illustrated that different dynamics of the rotor resulting from different faults of unbalance, rotor-stator rub and hydrodynamic bearing oil instabilities can be detected and classified using the shape-based approach. It is clearly illustrated that the multi-dimensional measurement technique provides insights into the behaviour of a rotor system, as confirmed by uniaxial conventional proximity probe measurements. The proposed approach complemented the widely used proximity probe sensing technique in terms of investigating rotor systems. An extension of the approach from 2D to 3D is also presented, starting with analysing how different shape descriptors influence the form of contours representing blade shapes in 3D. A detailed numerical investigation in which a Finite Element (FE) model of a physical rotor is analysed for changes in dynamic behaviour resulting from introduced damage in the blades, is conducted. The FE environment provides a platform in which the procedures for 3D shape analysis can be developed and tested before the proposed approach can be implemented experimentally. An experimental study that involves the use of a calibrated system of high-speed cameras to synchronously capture stereoscopic images of a rotating turbomachine is then presented. Variations in the dynamics of rotating blades are investigated and through a revolution based Principal Component Analysis (PCA) of SPCDs, the feasibility of a shape-based condition monitoring approach for turbine blades is illustrated. A comparative study to investigate the performance of PCA of SPCDs in relation to Kernel Principal Component Analysis (KPCA) is also conducted, and it is shown that KPCA outperforms PCA in terms of classifying different blade faults. The feasibility of using Multi-domain Statistical Features (MSFs) as feature vectors to which PCA or KPCA is applied for classification purposes is also presented. Results indicating how well different blade damage modes can be distinguished are provided, and it is clearly illustrated that MSFs are more robust to noise contamination in the signals compared to using the raw SPCDs time data.